Codevelopment Versus Outsourcing: Who Should Innovate in Supply Chains
Bibliographic record
Abstract
Outsourcing has long been a strategy to decrease cost. Increasingly firms recognize the value in their supply chains and call on suppliers to innovate, both in products and processes. Innovation to increase quality and demand or to reduce costs is critical to firm and supply chain success. In a two-stage supply chain, we investigate the impact of focal firm and supplier innovation costs (and capabilities) on the type of outsourcing chosen and the resulting investments in process and product innovation. The focal firm determines whether to perform design (including product innovation in the form of quality enhancement) and manufacturing (including process innovation in the form of cost reduction) in-house, to outsource manufacturing/process innovation while insourcing design/product innovation, to outsource both manufacturing/process innovation and design/product innovation, or to codevelop product innovation while outsourcing manufacturing/process innovation. We also examine the conditions under which codevelopment is favorable, given the supply chain faces potential positive and negative synergies from either the colocation of the innovation activities or costs of collaboration. After characterizing the optimal outsourcing decision, we find that the decision to outsource is more nuanced than simply which activities to outsource but must include options to collaborate on particular activities and specifically product innovation. We offer the insight to managers that codevelopment, despite the costs of collaboration, can benefit the firm and result in higher profits. This occurs through the improvement of demand via higher quality products.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".